SoroushVahidi/scidocs huggingface dataset:
Training verified learners with learned verifiers
(2018)

This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand, e.g., predicting labels given inputs, and a verifier network that computes a bound on how well the predictor satisfies the properties being verified. Both networks can be trained simultaneously to optimize a weighted combination of the standard data-fitting loss and a term that bounds the maximum violation of the property. Experiments show that not only is the predictor-verifier architecture able to train networks to achieve state of the art verified robustness to adversarial examples with much shorter training times (outperforming previous algorithms on small datasets like MNIST and SVHN), but it can also be scaled to produce the first known (to the best of our knowledge) verifiably robust networks for CIFAR-10.
Fingerprint Template Protection: From Theory to Practice
(2013)

One of the potential vulnerabilities in a biometric system is the leakage of biometric template information, which may lead to serious security and privacy threats. Most of the available template protection techniques fail to meet all the desired requirements of a practical biometric system like revocability, security, privacy, and high matching accuracy. In particular, protecting the fingerprint templates has been a difficult problem due to large intra-user variations (e.g., rotation, translation, nonlinear deformation, and partial prints). There are two fundamental challenges in any fingerprint template protection scheme. First, we need to select an appropriate representation scheme that captures most of the discriminatory information, but is sufficiently invariant to changes in finger placement and can be secured using available template protection algorithms. Secondly, we need to automatically align or register the fingerprints obtained during enrollment and matching without using any information that could reveal the features, which uniquely characterize a fingerprint. This chapter analyzes how these two challenges are being addressed in practice and how the design choices affect the trade-off between the security and matching accuracy. Though much progress has been made over the last decade, we believe that fingerprint template protection algorithms are still not sufficiently robust to be incorporated into practical fingerprint recognition systems. Anil K. Jain Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824, USA and Department of Brain & Cognitive Engineering, Korea University, Seoul. e-mail: jain@cse.msu.edu, Karthik Nandakumar Institute for Infocomm Research, A*STAR, Fusionopolis, Singapore. e-mail: knandakumar@i2r.astar.edu.sg, Abhishek Nagar Department of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824, USA. e-mail: nagarabh@cse.msu.edu
Programming cells by multiplex genome engineering and accelerated evolution
(2009)

The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-d-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.
Anti-inflammatory effect of Moringa oleifera Lam. seeds on acetic acid-induced acute colitis in rats
(2014)

OBJECTIVE
Anti-inflammatory, immuno-modulatory, and antioxidant properties of Moringa oleifera Lam. suggest that it might have beneficial effects on colitis. The present study was performed to investigate the anticolitis effect of Moringa oleifera seeds hydro-alcoholic extract (MSHE) and its chloroform fraction (MCF) on acetic acid-induced colitis in rats.


MATERIALS AND METHODS
Both MSHE and MCF with three increasing doses (50, 100, and 200 mg/kg) were administered orally to separate groups of male Wistar rats, 2 h before ulcer induction (using acetic acid 4%) and continued for 5 days. Prednisolone (4 mg/kg) and normal saline (1 ml/kg) were used in reference and control groups, respectively. All rats were sacrificed 24 h after the last dose (at day 6) and tissue injuries were assessed macroscopically and pathologically.


RESULTS
Extracts with three doses mentioned before were effective to reduce weight of distal colon (8 cm) as a marker for inflammation and tissue edema. Three doses of MSHE and two greater doses of MCF (100 and 200 mg/kg) were effective to reduce ulcer severity, area, and index as well as mucosal inflammation severity and extent, crypt damage, invasion involvement, total colitis index, and MPO activity compared with controls. MCF (50 mg/kg) was not significantly effective in reducing evaluated parameters of colitis compared with controls.


CONCLUSION
It is concluded that MSHE and MCF were both effective to treat experimental colitis and this might be attributed to their similar major components, biophenols and flavonoids. Since the efficacy was evident even in low doses of MSHE, presence of active constituents with high potency in seeds is persuasive.
A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation
(2013)

Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.
Faster discovery of faster system configurations with spectral learning
(2017)

Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT’s innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors—less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2–10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.
Intelligent video surveillance for monitoring fall detection of elderly in home environments
(2008)

Video surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper, we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head position, would provide a useful cue for detection of different behaviors. Extracted feature vectors are fed to a MLP neural network for precise classification of motions and determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
Binomial filters
(1996)

Binomial filters are simple and efficient structures based on the binomial coefficients for implementing Gaussian filtering. They do not require multipliers and can therefore be implemented efficiently in programmable hardware. There are many possible variations of the basic binomial filter structure, and they provide a wide range of space-time trade-offs; a number of these designs have been captured in a parametrised form and their features are compared. This technique can be used for multi-dimensional filtering, provided that the filter is separable. The numerical performance of binomial filters, and their implementation using field-programmable devices for an image processing application, are also discussed.
Cyber Bullying Prevention: Intervention in Taiwan
(2013)

BACKGROUND
This study aimed to explore the effectiveness of the cyber bullying prevention WebQuest course implementation.


METHODOLOGY/FINDINGS
The study adopted the quasi-experimental design with two classes made up of a total of 61 junior high school students of seventh grade. The study subjects comprised of 30 students from the experimental group and 31 students from the control group. The experimental group received eight sessions (total 360 minutes) of the teaching intervention for four consecutive weeks, while the control group did not engage in any related courses. The self-compiled questionnaire for the student's knowledge, attitudes, and intentions toward cyber bullying prevention was adopted. Data were analysed through generalized estimating equations to understand the immediate results on the student's knowledge, attitudes, and intentions after the intervention. The results show that the WebQuest course immediately and effectively enhanced the knowledge of cyber bullying, reduced the intentions, and retained the effects after the learning. But it produced no significant impact on the attitude toward cyber bullying.


CONCLUSIONS/SIGNIFICANCE
The intervention through this pilot study was effective and positive for cyber bulling prevention. It was with small number of students. Therefore, studies with large number of students and long experimental times, in different areas and countries are warranted.
Applications of Dual Quaternions in Three Dimensional Transformation and Interpolation
(2013)

Quaternions have long been integral to the field of computer graphics, due to their minimal and robust representation of rotations in three dimensional space. Dual quaternions represent a compact method of representing rigid body transformations (that is rotations and translations) with similar interpolation and combination properties. By comparing them to two other kinds of rigid transformations, we examine their properties and evaluate their usefulness in a real time environment. These properties include accuracy of operations, efficiency of operations, and the paths that interpolation and blending methods using those transformation methods take. The blending and interpolation methods are of particular interest as we constructed a skeletal animation system to highlight a potential application of dual quaternions. The bone hierarchy was constructed with dual quaternions and a sequence of identical hierarchies with different transformations at each bone can be interpolated as though they were keyframes to produce animations. Weighted transformations required in skinning the skeleton structure to a triangular mesh also prove an effective application of dual quaternions. Our findings show that while dual quaternions are useful in the context of skeletal animation, other applications may favour other representations, due to simplicity or speed.
Depth camera based indoor mobile robot localization and navigation
(2012)

The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.
1 TOURISM ONTOLOGY AND SEMANTIC MANAGEMENT SYSTEM : STATE-OFTHE-ARTS ANALYSIS
(2008)

The global importance of tourism is steadily rising,creating new job opportunities in many countries. Today’s information management solutions for the complex tasks of tourism intermediaries are still at an early stage from a semantic point of view. This paper presents some preliminary results of OnTourism Project. OnTourism is aimed at (1) applying, concretizing and evaluating Semantic Web technologies such as ontologies, semantic annotation of content, and semantic search to the information-rich and economically important tourism domain, (2) identifying, developing and integrating reference ontologies for the tourism industry, and (3) showing the proof-of-concept in a real-world scenario of the Austrian tourism industry. First results presented in this paper identify publicly available tourism ontologies and existing freely available ontology management tools for the tourism domain. We identify seven tourism ontologies which are suitable as a basis for creating problem-specific ontologies. Furthermore we review and evaluate five freely available ontology management tools that are suited for application in the tourism domain.
Compact Personalized Models for Neural Machine Translation
(2018)

We propose and compare methods for gradientbased domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture—combining a state-of-the-art self-attentive model with compact domain adaptation—provides high quality personalized machine translation that is both space and time efficient.
Deep generative-contrastive networks for facial expression recognition
(2017)

As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image that is estimated based on the given image. Consequently, we deploy deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner. In our proposed networks, we attempt to disentangle a facial expressive factor in two steps including learning of a generator network and a contrastive encoder network. We conducted extensive experiments on publicly available face expression databases (CK+, MMI, Oulu-CASIA, and in-the-wild databases) that have been widely adopted in the recent literatures. The proposed method outperforms the known state-of-the art methods in terms of the recognition accuracy.
Continuous noninvasive glucose monitoring technology based on "occlusion spectroscopy".
(2007)

BACKGROUND
A truly noninvasive glucose-sensing device could revolutionalize diabetes treatment by leading to improved compliance with recommended glucose levels, thus reducing the long-term complications and cost of diabetes. Herein, we present the technology and evaluate the efficacy of a truly noninvasive device for continuous blood glucose monitoring, the NBM (OrSense Ltd.).


METHODS
In vitro analysis was used to validate the technology and algorithms. A clinical study was performed to quantify the in vivo performance of the NBM device. A total of 23 patients with type 1 (n = 12) and type 2 (n = 11) diabetes were enrolled in the clinical study and participated in 111 sessions. Accuracy was assessed by comparing NBM data with paired self-monitoring of blood glucose meter readings.


RESULTS
In vitro experiments showed a strong correlation between calculated and actual glucose concentrations. The clinical trial produced a total of 1690 paired glucose values (NBM vs reference). In the paired data set, the reference glucose range was 40-496 mg/dl. No systematic bias was found at any of the glucose levels examined (70, 100, 150, and 200 mg/dl). The mean relative absolute difference was 17.2%, and a Clarke error grid analysis showed that 95.5% of the measurements fall within the clinically acceptable A&B regions (zone A, 69.7%; and zone B, 25.7%).


CONCLUSIONS
This study indicates the potential use of OrSense's NBM device as a noninvasive sensor for continuous blood glucose evaluation. The device was safe and well tolerated.
Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents
(2016)

Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of stringto-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person’s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
(2017)

The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multiagent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.